Network traffic analysis is fundamental for network management, troubleshooting, and security. Tasks such as traffic classification, anomaly detection, and novelty discovery are fundamental for extracting operational information from network data and measurements. We witness the shift from deep packet inspection and basic machine learning to Deep Learning (DL) approaches where researchers define and test a custom DL architecture designed for each specific problem. We here advocate the need for a general DL architecture flexible enough to solve different traffic analysis tasks. We test this idea by proposing a DL architecture based on generic data adaptation modules, followed by an integration module that summarises the extracted information into a compact and rich intermediate representation (i.e. embeddings). The result is a flexible Multi-modal Autoencoder (MAE) pipeline that can solve different use cases. We demonstrate the architecture with traffic classification (TC) tasks since they allow us to quantitatively compare results with state-of-the-art solutions. However, we argue that the MAE architecture is generic and can be used to learn representations useful in multiple scenarios. On TC, the MAE performs on par or better than alternatives while avoiding cumbersome feature engineering, thus streamlining the adoption of DL solutions for traffic analysis.

网络流量分析是网络管理、故障排除和安全性的基础。本文提出了一种基于多模态自动编码器（MAE）的深度学习（DL）体系结构，用于解决不同的流量分析任务，其中包括流量分类（TC）任务。研究结果表明，MAE体系结构在TC任务中能够与现有解决方案相媲美甚至更好，同时避免了繁琐的特征工程，从而简化了采用DL方法进行流量分析的过程。

网络流量分析的通用多模式表示学习